1. Field of the Invention
This invention relates generally to the field of semiconductor device manufacturing and, more particularly, to a method and apparatus for determining product-specific error and tool drift.
2. Description of the Related Art
A variety of processing tools are used to fabricate a semiconductor device. The processing tools may include photolithography steppers, etch tools, deposition tools, polishing tools, rapid thermal processing tools, ion implantation tools, and the like. Wafers (or wafer lots) are processed in the tools in a predetermined order and each processing tool modifies the wafers according to a particular operating recipe. For example, a photolithography stepper may be used to form a patterned layer of photoresist above the wafer. Features in the patterned layer of photoresist correspond to a plurality of features, e.g. gate electrode structures, which will ultimately be formed above the surface of the wafer. For another example, a deposition tool may be used to form a layer of material above the wafer or above gate electrode structures that have been formed on the wafer.
The operating recipe may be determined using a process model that relates one or more input parameters associated with the processing tool with one or more output parameters. For example, a thickness of the deposited layer (T) may be related to a deposition time (t) by the process model equation T=Rt, where R is the deposition rate associated with the deposition tool. Input parameters of the processing tools are selected to attempt to achieve a desired target value for the output parameters. For example, the deposition tool may have a deposition rate of approximately 50 angstroms per second. Accordingly, a deposition time of approximately four seconds may be selected so that the thickness of the deposited layer is approximately equal to a desired target value for the thickness of 200 angstroms. The parameters of the process model may change as the process tool is used to process additional lots of wafers due to a phenomenon known as “tool drift.” For example, the deposition rate of a deposition tool may decrease as additional lots of wafers are processed in the deposition tool. Conventional processing tools may estimate the associated process tool drift and use this estimate to modify the parameters of the process model as additional lots of wafers are processed.
Measured values of the output parameters typically differ from the values predicted by the process model. For example, after four seconds, the deposition tool having a nominal deposition rate of 50 angstroms per second may have only deposited a layer that is 192 angstroms thick, as opposed to the target thickness of 200 angstroms. The difference between the measured values of the output parameters and the values predicted by the process model is referred to as the model error. The portion of the model error associated with the process tool may also vary as the process tool is used to process additional lots of wafers, at least in part because of the tool drift. Thus, the portion of the model error associated with the process tool is commonly referred to as the “tool drift error.”
The model error may also vary between different products processed in the process tool. For example, a processing tool may be used to produce multiple versions of a 64 MB flash memory device. The different versions of the flash memory device, i.e. the different products, may utilize a different layout of individual gate features, may have a different density of the features, may implement the memory elements using different structures, and the like. Although the deposition rate of the deposition tool should remain approximately constant, variations in the underlying structure may cause the thickness of the deposited layer to vary. For example, the thickness of a layer deposited over a relatively dense array of features in a given time period may be larger than the thickness of a layer deposited over a relatively sparse array of features in the same time period. Thus, the model error associated with the product having the relatively sparse array of features may be larger that the model error associated with the product having the relatively dense array of features.
In many cases, conventional process models assume that the model error is due to random noise, which averages to zero and can therefore be ignored. Alternatively, the model error due to random noise may be calculated and included in the model. However, conventional process models cannot distinguish between product errors and tool errors. Consequently, if the process tool is used to process wafers for more than one product, the accuracy of the process model may be reduced for at least some of the products. For example, partially processed wafers associated with first and second products may be processed by a deposition tool having an approximately constant deposition rate. However, the thickness of a layer formed on the partially processed wafer associated with the first product may be less than the thickness of a layer formed on the partially processed wafer associated with the second product because the first and second products have different product errors.
Since the conventional process models described above cannot distinguish between product errors and tool errors, the process models cannot be adjusted to compensate strictly for product-to-product variations or tool drift in the model errors. Consequently, errors in the thickness of the layers formed by the deposition tool may be increased by the failure to account for the product-specific variations in the model errors, which may decrease the efficiency of the processing tool and the associated fabrication process.
The present invention is directed to addressing the effects of one or more of the problems set forth above.